Abstract
Metaphor detection has been a challenging task in the NLP domain both before and after the emergence of transformer-based language models. The difficulty lies in subtle semantic nuances that are required to detect metaphor and in the scarcity of labeled data. We explore few-shot setups for metaphor detection, and also introduce new question answering data that can enhance classifiers that are trained on a small amount of data. We formulate the classification task as a question-answering one, and train a question-answering model. We perform extensive experiments for few shot on several architectures and report the results of several strong baselines. Thus, the answer to the question posed in the title is a definite “Yes!”- Anthology ID:
- 2022.flp-1.17
- Volume:
- Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Debanjan Ghosh, Beata Beigman Klebanov, Smaranda Muresan, Anna Feldman, Soujanya Poria, Tuhin Chakrabarty
- Venue:
- Fig-Lang
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 125–130
- Language:
- URL:
- https://aclanthology.org/2022.flp-1.17
- DOI:
- 10.18653/v1/2022.flp-1.17
- Cite (ACL):
- Lena Dankin, Kfir Bar, and Nachum Dershowitz. 2022. Can Yes-No Question-Answering Models be Useful for Few-Shot Metaphor Detection?. In Proceedings of the 3rd Workshop on Figurative Language Processing (FLP), pages 125–130, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Can Yes-No Question-Answering Models be Useful for Few-Shot Metaphor Detection? (Dankin et al., Fig-Lang 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.flp-1.17.pdf